niltecedu

That was the first version they imrpvoed after that

I just dont go there at all because they added the weird copied of stackoverflow feature when copying code from there. Like even if I want to use its actively hostile, I need to paste it somewhere remove all of the extra things added and then copy properly

ffffffff.... damnit I love python but why is it so difficult like this. JIT would have been so perfect so the language, increasingly overtime with AI code coming through I am not sure not focusing on making the performance better is going to be better for longer term. Even though a lot of the other operations done in python are done faster in libraries I would still like faster startup times, or faster loops.

Yeah but podcast is low quality time; shows,movies are high quality, games are high quality high focus, music and podcast are lower on the rung. Its something you can have playing in the background and not pay attention.

Like the time I have for high quality stuff and low stuff are different and therefore different audiences. For Low quality stuff podcasts are fighting against tiktok

Not really, the time I set aside for shows, podcast, movies and games is all different. I am not going to give up watching slow horses to watch a podcast,

Some HPC Clusters, xgboost, read pretrained model from NFSRDMA Server, read data, parallelise over MPI with one process per model. And the latest Zen5 Epycs. I could do it all on just one of the 192 cores EPYCs but we like to parallelise it over multiple machines to get the extra speed and reliablity.

It wasn't that expensive, the main problem was the storage bottleneck which got fixed with just some LVM cache and gen5 ssds.

They are very dynamic, like too dynamic but we had really good predictors for it based on our existing data and past historical data.

Yep, tried them multiple times but too many tradeoffs and not matching rmse kills it for us. MAE isn't that impressive for us.

But why is the question, I run about 400k models every 30 minutes at work. I have tried all other approaches, but xgboost model per timeseries has never been beaten before. Even if I wasn't targetting accuracy, Xgboost loading a pretrained model and predicting with it and still faster.

I forecast 400k timeseries every 30minutes at work.

system operator and the transmission operator, system operator keep throwing a wrench into pre-made plans, transmission operator are a complete monopoly who bully their way around

I dont see why people arent mentioning edge in the comments? I think with e5 license and purview, its the same as what I am seeing mentioned in Firefox Enterprise?

Not unsurprising, excluding grid efficiency and industrial demand, its also just households. More and more places are getting batteries wherever possible, plug in solar is also avaialble. The amount of demand I don't forsee increasing at all unless people start getting air source heatpumps.

National Grid can mean NGED, NGET or NESO, god I love the UK. 2/3 of those are completely cuckoo regarding long term planning so not surprising to hear those comments.

A lot of our came from non cs backgrounds with a lot paper writing, pandas and numpy makes sense to them, its like writing Latex for them, polars is not. Its changing recently as we move towards more AI code but thats how/why things used to be.